Whole genome data will soon be available for tens to hundreds of thousands of individuals. This information is unprecedented in its ability to understand individual risk factors for disease. However, the volume of these data presents several major challenges to its interpretation. One powerful approach for interpreting genomes and identifying functional variants is to combine whole genome data with functional genomics or multi-omics data. Our research project focuses on multi-omics analyses in the TOPMED project to improve our understanding of individual and environmental genetic risk factors in heart, lung, blood and sleep (HLBS) disorders. In Aim 1, we will apply multi-omic outlier analysis to identify rare variants with large effects on multi-omics phenotypes. We will apply approaches we have developed in GTEx and SardiNIA that integrate genome and functional genomics data. Our premise is that rare genetic variants with large effects on -omics phenotypes will be strong candidates to contribute to an individual's risk of genetic disease. Using these rare variants, we propose to improve understanding of the combined effects of common and rare variants in HLBS disorders. In Aim 2, we will apply and advance software we have developed to improve the mapping of gene- by-environment (GxE) and gene-by-gene (GxG) effects. Specifically, we have demonstrated that allele-specific signals have improved power for identifying both GxE and GxG genes and variants and we will apply our model to both transcriptome and methylome data in TOPMED to identify diverse hits for observed and latent environments. We will further conduct analyses to identify GxE hits for measured metabolites and, overall, with respect to differences in ancestry. Our premise is that GxE variants identified through multi-omics data analysis will define or modify genetic risk factors for HLBS and other disorders. The impact of discovered GxE and GxG variants will be evaluated through association analyses in the entire TOPMED cohort. Our activities will bring new opportunities to study and understand both individual and gene-by- environment effects influencing disease risk. By leveraging multi-omics data, we will integrate rare variant and gene-by-environment analyses within TOPMED; an activity that would typically require enormous investment and hundreds of thousands of samples if conducted with only genetic data. All software, pipelines and research results developed by our group will be rapidly available on standard websites, in the Cloud and available to support collaborative efforts within TOPMED and the larger research community. Further, as our team has extensive experience with large-scale genomics and functional genomics analysis, we will provide assistance and effort in implementing world-class analytical pipelines and further complement TOPMED with data from GTEx, MoTrPAC, DGN, WHI and other project data to enhance the power of analyses. Our effort will provide multiple avenues, from rare variants to environmental genetics, to aid in interpreting whole genomes and the impact of genetic variation in health and disease.